text: /**
   * Copyright 2021 Huawei Technologies Co., Ltd
   *
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   */
  
  #ifndef MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_GPU_NN_ADAM_WEIGHT_DECAY_GPU_KERNEL_H_
  #define MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_GPU_NN_ADAM_WEIGHT_DECAY_GPU_KERNEL_H_
  
  #include <vector>
  #include <string>
  #include "backend/kernel_compiler/gpu/gpu_kernel.h"
  #include "backend/kernel_compiler/gpu/gpu_kernel_factory.h"
  #include "backend/kernel_compiler/gpu/cuda_impl/adam_impl.cuh"
  namespace mindspore {
  namespace kernel {
  constexpr size_t INPUT_NUM = 9;
  template <typename T>
  class AdamWeightDecayGpuKernel : public GpuKernel {
   public:
    AdamWeightDecayGpuKernel()
        : variable_size_(0),
          m_size_(0),
          v_size_(0),
          learning_rate_size_(0),
          beta1_size_(0),
          beta2_size_(0),
          epsilon_size_(0),
          decay_size_(0),
          gradient_size_(0),
          is_null_input_(false),
          kernel_name_("AdamWeightDecay") {}
  
    ~AdamWeightDecayGpuKernel() override = default;
  
    const std::vector<size_t> &GetInputSizeList() const override { return input_size_list_; }
    const std::vector<size_t> &GetOutputSizeList() const override { return output_size_list_; }
    const std::vector<size_t> &GetWorkspaceSizeList() const override { return workspace_size_list_; }
  
    bool Launch(const std::vector<AddressPtr> &inputs, const std::vector<AddressPtr> &, const std::vector<AddressPtr> &,
                void *stream_ptr) override {
      if (is_null_input_) {
        return true;
      }
      T *variable = GetDeviceAddress<T>(inputs, 0);
      T *m = GetDeviceAddress<T>(inputs, 1);
      T *v = GetDeviceAddress<T>(inputs, 2);
      float *lr = GetDeviceAddress<float>(inputs, 3);
      float *beta1 = GetDeviceAddress<float>(inputs, 4);
      float *beta2 = GetDeviceAddress<float>(inputs, 5);
      float *epsilon = GetDeviceAddress<float>(inputs, 6);
      float *decay = GetDeviceAddress<float>(inputs, 7);
      T *gradient = GetDeviceAddress<T>(inputs, 8);
      AdamWeightDecayOp(inputs[0]->size / sizeof(T), gradient, lr, beta1, beta2, epsilon, decay, variable, m, v,
                        reinterpret_cast<cudaStream_t>(stream_ptr));
      return true;
    }
  
    bool Init(const CNodePtr &kernel_node) override {
      kernel_name_ = AnfAlgo::GetCNodeName(kernel_node);
      size_t input_num = AnfAlgo::GetInputTensorNum(kernel_node);
      if (input_num != INPUT_NUM) {
        MS_LOG(EXCEPTION) << "For '" << kernel_name_ << "', the number of inputs should be " << INPUT_NUM << ", but got "
                          << input_num;
      }
  
      variable_size_ = sizeof(T);
      m_size_ = sizeof(T);
      v_size_ = sizeof(T);
      learning_rate_size_ = sizeof(float);
      beta1_size_ = sizeof(float);
      beta2_size_ = sizeof(float);
      epsilon_size_ = sizeof(float);
      decay_size_ = sizeof(float);
      gradient_size_ = sizeof(T);
  
      auto variable_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0);
      auto m_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 1);
      auto v_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 2);
      auto gradient_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 8);
      is_null_input_ = CHECK_SHAPE_NULL(variable_shape, kernel_name_, "var") ||
                       CHECK_SHAPE_NULL(m_shape, kernel_name_, "m") || CHECK_SHAPE_NULL(v_shape, kernel_name_, "v") ||
                       CHECK_SHAPE_NULL(gradient_shape, kernel_name_, "gradient");
      if (is_null_input_) {
        InitSizeLists();
        return true;
      }
      for (size_t i = 0; i < variable_shape.size(); i++) {
        variable_size_ *= variable_shape[i];
      }
  
      for (size_t i = 0; i < m_shape.size(); i++) {
        m_size_ *= m_shape[i];
      }
  
      for (size_t i = 0; i < v_shape.size(); i++) {
        v_size_ *= v_shape[i];
      }
  
      for (size_t i = 0; i < gradient_shape.size(); i++) {
        gradient_size_ *= gradient_shape[i];
      }
  
      InitSizeLists();
      return true;
    }
  
   protected:
    void InitSizeLists() override {
      input_size_list_.push_back(variable_size_);
      input_size_list_.push_back(m_size_);
      input_size_list_.push_back(v_size_);
      input_size_list_.push_back(learning_rate_size_);
      input_size_list_.push_back(beta1_size_);
      input_size_list_.push_back(beta2_size_);
      input_size_list_.push_back(epsilon_size_);
      input_size_list_.push_back(decay_size_);
      input_size_list_.push_back(gradient_size_);
      output_size_list_.push_back(0);
      output_size_list_.push_back(0);
      output_size_list_.push_back(0);
    }
  
   private:
    size_t variable_size_;
    size_t m_size_;
    size_t v_size_;
    size_t learning_rate_size_;
    size_t beta1_size_;
    size_t beta2_size_;
    size_t epsilon_size_;
    size_t decay_size_;
    size_t gradient_size_;
    bool is_null_input_;
    std::string kernel_name_;
  
    std::vector<size_t> input_size_list_;
    std::vector<size_t> output_size_list_;
    std::vector<size_t> workspace_size_list_;
  };
  }  // namespace kernel
  }  // namespace mindspore
  
  #endif  // MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_GPU_NN_ADAM_WEIGHT_DECAY_GPU_KERNEL_H_
  